Mutually Beneficial Confluent Routing

We investigate a new multi-user routing problem: mutually beneficial confluent routing (MCR). In the MCR, every user has his/her own source and destination; confluences of user routes occur so that users can mutually benefit from travelling together on the confluences. The idea of gaining benefit from travelling together is valuable in various practical applications, such as ride sharing, delivery routing, and pedestrian navigation. We formulate the MCR as a new combinatorial optimization problem on road networks. The MCR is more general and complex than single vehicle routing problems, ride-sharing problems, and the Steiner tree problem. We propose exact and efficient algorithms for the MCR for the setting of two or three users. The setting is reasonable for various practical applications. The key ideas of our algorithms are to use “confluence patterns” of the optimal solutions and exploit the properties of geometric graphs. Experimental results obtained on large scale road networks reveal that our algorithms are sufficiently efficient.

[1]  Martin W. P. Savelsbergh,et al.  The General Pickup and Delivery Problem , 1995, Transp. Sci..

[2]  Yi Lu,et al.  Path Problems in Temporal Graphs , 2014, Proc. VLDB Endow..

[3]  Yossi Azar,et al.  The Price of Routing Unsplittable Flow , 2005, STOC '05.

[4]  Erez Karpas,et al.  Towards Rational Deployment of Multiple Heuristics in A* (Extended Abstract) , 2013, SOCS.

[5]  Andrew V. Goldberg,et al.  Robust Distance Queries on Massive Networks , 2014, ESA.

[6]  Shuigeng Zhou,et al.  Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation , 2012, Proc. VLDB Endow..

[7]  Lars Völker,et al.  Fast Detour Computation for Ride Sharing , 2010, ATMOS.

[8]  Mario A. Nascimento,et al.  A Mixed Breadth-Depth First Search Strategy for Sequenced Group Trip Planning Queries , 2015, 2015 16th IEEE International Conference on Mobile Data Management.

[9]  Dennis Luxen,et al.  Multi-Hop Ride Sharing , 2013, SOCS.

[10]  Björn Zenker,et al.  Calculating Meeting Points for Multi User Pedestrian Navigation Systems , 2011, KI.

[11]  Tanzima Hashem,et al.  Group Trip Planning Queries in Spatial Databases , 2013, SSTD.

[12]  Manuel López-Ibáñez,et al.  The travelling salesman problem with time windows: Adapting algorithms from travel-time to makespan optimization , 2013, Appl. Soft Comput..

[13]  Paul S. Bonsma,et al.  A Constant Factor Approximation Algorithm for Unsplittable Flow on Paths , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[14]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[15]  Richard F. Hartl,et al.  A survey on pickup and delivery problems , 2008 .

[16]  Richard F. Hartl,et al.  A survey on pickup and delivery problems , 2008 .

[17]  Mohammad Taghi Hajiaghayi,et al.  Approximation Algorithms for Nonuniform Buy-at-Bulk Network Design , 2010, SIAM J. Comput..

[18]  Wilfred Ng,et al.  Efficient processing of optimal meeting point queries in Euclidean space and road networks , 2013, Knowledge and Information Systems.

[19]  Tanzima Hashem,et al.  Efficient Computation of Group Optimal Sequenced Routes in Road Networks , 2015, 2015 16th IEEE International Conference on Mobile Data Management.

[20]  Pablo Cortés,et al.  Optimal algorithm for the demand routing problem in multicommodity flow distribution networks with diversification constraints and concave costs , 2013 .

[21]  Vassilis J. Tsotras,et al.  Parameterized algorithms for generalized traveling salesman problems in road networks , 2013, SIGSPATIAL/GIS.

[22]  H. Pollak,et al.  Steiner Minimal Trees , 1968 .

[23]  Fernando A. C. C. Fontes,et al.  Concave minimum cost network flow problems solved with a colony of ants , 2013, J. Heuristics.

[24]  Mohamed F. Mokbel,et al.  SHAREK: A Scalable Dynamic Ride Sharing System , 2015, 2015 16th IEEE International Conference on Mobile Data Management.